Network node ad targeting

ABSTRACT

A computer-implemented method for displaying advertisements to members of a network comprises identifying one or more communities of members, identifying one or more influencers in the one or more communities, and placing one or more advertisements at the profiles of one or more members in the identified one or more communities.

RELATED APPLICATIONS

This application is a continuation application and claims priority under35 USC §120 to U.S. patent application Ser. No. 11/618,506, filed onDec. 29, 2006, the entire contents of which are hereby incorporated byreference.

TECHNICAL FIELD

In general, this document describes advertising to members incommunities within an online social network by displaying advertisementsrelevant to the common interest of the members of the community. In oneexample, a system may receive the profiles of all the members of anonline social network, identify communities of members, determine thecommon interest of members of the community and display advertisementsrelevant to each community on the profiles of the members within thecommunity.

BACKGROUND

As the Internet has become increasingly popular, online social networksare becoming an important and pervasive mechanism for communication,entertainment, and professional and social networking. Members of anetwork implicitly associate or explicitly link themselves with one ormore members within the network based on factors such as commoninterests. Interaction and signaling between members either directly orthrough other members cause the formation of communities of commoninterests within the online social network. The members of suchcommunities are connected by one or more common interests.

Factors such as member interactions, content on member profiles,dynamically changing size of the community, and the like establish ahierarchy within a community where certain members are more popular thanothers and, consequently, wield enhanced influence over other members inthe community.

SUMMARY

The present inventors recognized that blanket advertising across thenetwork tends not to be cost-efficient to advertisers, since the membersof the network tend not to be interested in the products and servicesbeing advertised unless the advertisements are relevant to the members'interest. Presenting to advertisers a community of members sharing acommon interest provides advertisers with an opportunity to presentproducts and services of interest to the members of the network, therebymaximizing the return on the investment made to advertising.

The present inventors also recognized that advertising to members of anonline social network based solely on the content of their profile lackstargeted generation of advertisements. Members of a community may havecontent on their profile in addition to the common interest of thecommunity, such as personal information, etc. Relying solely on thecontent of the profile of a member of a community decreases thespecificity of advertisements to the community since the presence ofadditional information distorts the signal from the content related tothe common interests.

In one implementation, a computer-implemented method for displayingadvertisements to members of a network is described. The method includesidentifying one or more communities within a network, wherein thecommunity can include a plurality of members of the network, identifyingone or more influencers in the one or more communities, and placing oneor more advertisements at the profiles of one or more members in the oneor more communities.

This and other aspects can include one or more of the followingfeatures. The method can further include receiving informationpertaining to a plurality of members belonging to the network, whereinthe information comprises content of profiles of the plurality ofmembers and links between the plurality of members. Identifying one ormore communities can include grouping members based on the linksestablished between the members of the network. The link can connect afirst member with one or more members of the network. Each link can havea weight. The method can further include identifying one or more commoninterests of the plurality of members of a community based on thecontent of the profiles of the plurality of members. The method canfurther include ranking the plurality of members belonging to each ofthe one or more communities based on the links between the members ofthe community. The influencer can be a the member with the highest rank.The network can include an online social network. The profile cancomprise one or more web pages stored on a server hosting the onlinesocial network. A member of the network can belong to one or more of theplurality of communities. The one or more advertisements can be placedsolely on the profiles of the one or more influencers.

In another aspect, an advertisement server for displaying advertisementsto members of a network is described. The advertisement server includesa community identifier configured to identify one or more communities,and identify one or more influencers in the one or more communities, andan advertisement inventory configured to store advertisements to bedisplayed on the profiles of one or more members of the identifiedcommunities.

This and other aspects can include one or more of the followingfeatures. The advertisement server can further include a networkinventory configured to store the information pertaining to theplurality of members belonging to the network, wherein the informationincludes content of profiles of the plurality of members and linksbetween the plurality of members. The community identifier can furtherbe configured to group members based on the links established betweenthe members of the network. The link can connect a first member with oneor more members of the network. Each link can have a weight. Thecommunity identifier can further be configured to identify one or morecommon interests of the plurality of members of a community based on thecontent of the profiles of the plurality of members. The communityidentifier can further be configured to rank the plurality of membersbelonging to each of the one or more communities based on the linksbetween the members of each community. The influencer can be the memberwith the highest rank. The network can include an online social network.The profile can include one or more web pages stored on a server hostingthe online social network. A member of the network can belong to one ormore of the plurality of communities. The one or more advertisements canbe placed solely on the profiles of the one or more influencers.

In another aspect, an advertisement server for displaying advertisementsto members of a network is described. The advertisement server includesa means for identifying one or more communities, and identifying one ormore influencers in the one or more communities, and a means for storingadvertisements to be displayed on the profiles of one or more members ofthe identified communities.

This and other aspects can include one or more of the followingfeatures. The advertisement server can further include a means forstoring the information pertaining to the plurality of members belongingto the network, wherein the information comprises content of profiles ofthe plurality of members and links between the plurality of members. Themeans for identifying one or more communities and identifying one ormore influencers in the one or more communities is further configured togroup members based on the links established between the members of thenetwork. The link can connect a first member with one or more members ofthe network. Each link can have a weight. The means for identifying oneor more communities and identifying one or more influencers in the oneor more communities can further be configured to identify one or morecommon interests of the plurality of members of a community based on thecontent of the profiles of the plurality of members. The means foridentifying one or more communities and identifying one or moreinfluencers in the one or more communities is further configured to rankthe plurality of members belonging to each of the one or morecommunities based on the links between the members of each community.The influencer can be the member with the highest rank. The network caninclude an online social network. The profile can include one or moreweb pages stored on a server hosting the online social network. A memberof the network can belong to one or more of the plurality ofcommunities. The one or more advertisements can be placed solely on theprofiles of the one or more influencers.

The systems and techniques described here may provide one or more of thefollowing advantages. First, a system can identify communities of commoninterests within an online social network. This may allow advertisers totarget the communities based on the common interest of the community, asopposed to the content of individual profiles. Second, a system canidentify members who belong to overlapping communities as a result ofmore than one common interest. By blending advertisements relevant tothe interests of each of the overlapping communities, advertisers maytarget the members common to these communities. Third, a system canidentify the influencers from among the members of a community. This mayprovide advertisers with the option of targeting either all members inthe community or advertising only on the profile of the influencer,thereby targeting the entire community.

In addition, the system encourages members of communities in onlinesocial networks to enrich the content on their profiles. The presence ofhigh quality content relevant to shared interests on a member's profileincreases the popularity of the member in the community and improves amember's chances of being an influencer. An influencer may receivefinancial incentives from advertisers in exchange for permission todisplay advertisements on the member's profile.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,objects, and advantages will be apparent from the description anddrawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram of an example of a system for advertisingto communities in an online social network.

FIG. 2 is a schematic diagram of an example of a system for advertisingto communities in an online social network.

FIG. 3A is a schematic diagram of an example of a cluster of usersconnected by internal and external friendships.

FIG. 3B is a schematic diagram of an example of a cluster of usersconnected by internal and external friendships.

FIG. 3C is a schematic diagram of an example of a cluster of usersconnected by internal and external friendships.

FIG. 4 is a flow chart of an example of a method for advertising tocommunities in an online social network.

FIG. 5 is an example of profiles of members grouped into a communitybased on a common interest in movies.

FIG. 6 is an example of blending advertisements for display on theprofile of a member belonging to more than one community.

FIG. 7 is an example of designating one member as a network influencerof a community.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

FIG. 1 depicts a schematic diagram of an example of a system 100 foradvertising to communities in an online social network 102. An onlinesocial network 102 includes interconnected profiles of members 104, forexample, web pages stored of the network. Further, the online socialnetwork 102 includes hosted web pages that describe the profiles and thecharacteristics of the members 104 of the network 102. Examples ofonline social networks can include orkut.com, myspace.com, andfriendster.com. Alternatively, other types of online social networks orpersonal web pages may be used, such as job hunting web sites (e.g.,monster.com), school alumni web sites, organizations of professionals,Internet dating sites, ratings sites (e.g., hotornot.com), and a companyemployee internal web site.

The system 100 identifies one or more communities 106 of members 104within the online social network 102. Further, the system 100 identifiesone or more influencers of communities 106. The one or more influencersare identified based on factors including traffic to the profile, numberof friends, group membership, number of user interactions, popularitywithin the community and profile content.

The online social network 102 comprises the profiles of members 104. Thenetwork 102 is hosted by a server. The profile of a member 104 comprisesone or more web pages stored on the server and linked to profiles of oneor more members 104 of the network 102. In one implementation, thesystem 100 includes an advertisement server 108 to receive all theprofiles of members 104 of the online social network 102. Each member104 establishes a link with at least one other member 104. In oneaspect, a link is established when one member chooses another member asa friend. In another aspect, a link is established when one membervisits another members web page frequently. In another aspect, a link isestablished when one member communicates with another member through thenetwork. In yet another aspect, a link is established between twomembers when both members include themselves to the same community.

The network inventory 110 stores the links between members 104. Thenumber and type of links in the online social network 102 are dynamicsince the number of members 104 and the manner in which one member 104links to another member 104 constantly changes. In one aspect, theonline social network monitors and updates the links. The network sendsthe updated links to the advertisement server. The advertisement serveroverwrites the existing links with the new links and stores the links inthe network inventory. In another aspect, the advertisement servermonitors and updates the links and regularly overwrites the existinglinks in the network inventory with the new links.

In one implementation, a link between two members 104 has a weightassociated with it. In one aspect, the weight of a link depends on thetype of the link. For example, a link between two members where onemember has designated another member as a friend is associated moreweight than a link where one member occasionally visits another member'sweb page. In another aspect, the weights of links depend on the numberof links that connect one member to one or more members. In anotheraspect, the links are designated equal weight. The weight of the linkscan also depend on factors such as community membership, interestintersection, circle of friends, distance between friends, geographiclocation, and demographic location.

The advertisement server 108 includes a community identifier 112. In oneimplementation, the community identifier 112 generates a community 106based on the links between members in the network inventory 110. In oneaspect, two members of a community are directly linked. In anotheraspect, two members of a community are linked indirectly by associationthrough one or more members. In one implementation, the communityidentifier 112 employs graph theory and the network signals between themembers of the community to generate a community 106.

The community identifier 112 receives the graph connecting the membersof the network and the links between the members. Subsequently, thecommunity identifier 112 iteratively optimizes the community 106 toaccount for addition of new members 104 and removal of old members 104based on the links between the members 104. In this manner, thecommunity identifier 112 groups members 104 of the online social network102 into communities 106. In one aspect, members are added when membersof the existing community link to members that were not part of thecommunity during the initial generation. In another aspect, thecommunity is affected by changes in the types of links between members.For example, a first member who frequently visits a second member's webpage may designate the second member as a friend. This increases thestrength of the link between the two members affecting the structure ofthe community. The strength of a link is also altered when a firstmember, linked to a second member through a third member, establishes adirect link to the second member. The strength of a link may also dependon factors such as frequency of member interaction, actions such ascommunication between members through messages on profiles, one memberdeclaring themselves a second member's fan, and one member writingtestimonials about a second member on the second member's profile.

Based on the links and the weight of the links between the members 104,the community identifier 112 generates one or more communities 106. Theweight of the links change during the iterations due to the addition andsubtraction of members 104. The community identifier 112 optimizes theweight of the links between members 104 of the community 106 until thecommunity can no longer be improved. A community comprises at least twomembers. In addition, at least one member of the online social networkmay belong to more than one community.

Upon identifying the one or more communities 106 within the onlinesocial network 102, the advertisement server 108 identifies influencerswithin the community. In one implementation, the advertisement server108 ranks the web pages of the members 104 in a community 106. Theadvertisement server uses a ranking algorithm, such as PageRank, to rankthe members 104 in the community 106. The advertisement serverdesignates influencers based on the rank of the members 104. In oneaspect, the advertisement server designates one member as an influencer.In another aspect, the advertisement server designates more than onemember from among the top ranked members as equal influencers of thecommunity. In another aspect, the advertisement server associatesweights to the top ranked members of the community, wherein the weightis directly proportional to the rank of the member, and the weightrefers to the degree of influence of a member in the community.

The advertisement server 108 also stores the content of the profiles ofmembers 104 of the online social network in the network inventory 110.Based on the content of the profiles, the advertisement server 108identifies one or more common interests that connect the members 104 ofa community 106. For example, a member with content related to soccer onthe profile establishes links with other members with similar content ontheir profiles. A community of soccer fans is formed between theplurality of members. The community is characterized by the content onthe member profiles as well as the links between the members. Thecommunity identifier identifies this community, ranks the members basedon the types of links, and designates one or more influencers.Subsequently, the advertisement server designates soccer as a commoninterest of this community based on the content of the member profilesin the community.

Since the number of members and the content on the profiles of membersis dynamic, the content of the network inventory 110 requires regularupdating. In one aspect, the online social network monitors and updatesthe content on the profiles. The online social network transmits theupdated content to the advertisement server. The advertisement serveroverwrites the existing content stored in the network inventory with theupdated content. In another aspect, the advertisement server monitorsthe online social network, updates the content on the profiles andoverwrites the existing content in the network inventory with theupdated content.

The advertisement server 108 includes an advertisement inventory 114.The advertisement inventory 114 includes advertisements to be displayedon the one or more profiles of members 104 of the online social network102. In one implementation, the advertisement server 108 retrievesadvertisements from the advertisement inventory 114 that are relevant tothe common interest of a community 106 and transmits the advertisementsto the community identifier 112. The community identifier 112 identifiesone or more profiles of members 104 belonging to the community 106 todisplay the advertisements. In one aspect, the identified one or moremembers are the one or more influencers of the community. In anotheraspect, the identified one or more members include the influencers andother members of the community.

The community identifier 112 also identifies members belonging to morethan one community 106. Since a member 104 can establish links with morethan one member 104 in the online social network 102, and since linksare established based on the content of member profiles, one member 104can belong to more than one community 106, each community 106 having adifferent interest. The advertisement server 108 determines the commoninterest of the more than one community 106 to which the member 104belongs. The advertisement server 108 can display on the profiles ofsuch members, one or more advertisements that are relevant to the morethan one interest of the member 104.

In one aspect, the number of advertisements displayed on the memberprofile may represent the interests of the member equally. For example,if a member belongs to a community of soccer players and motorcycleriders, two advertisements related to soccer and two advertisementsrelated to motorcycle gear may be displayed on the member's profile. Inanother aspect, the number of advertisements displayed on the memberprofile may be affected by the weight of the links and the rank of themember in each community. For example, if the weight of the links andthe rank of the member in the soccer community is higher than those inthe motorcycle riders community, three advertisements related to soccerand one related to motorcycle gear may be displayed on the member'sprofile.

FIG. 2 depicts a schematic diagram of an example of a system 100 foradvertising to communities 106 in an online social network 102. Theadvertisement server 108 includes a web crawler 202. The web crawler 202receives the contents of member profiles 204 via an interface 206. Theweb crawler 202 traverses the content of the member profiles 204 andgenerates the indexed content 208 stored in the network inventory 110.In addition, the links 210 between the members 104 of the online socialnetwork 102 are also stored in the network inventory 110.

In addition, the web crawler 202 can generate statistical associationsbetween keywords and the content of the member profiles 204. Forexample, the content of the member profiles 204 can contain informationused by the web crawler 202 to identify what keyword may be related tothe content of the member profile. This information can include textwithin the profile, keywords (e.g., metadata) that describe the profile,frequencies of words occurring in the profile, font size of text in theprofile (e.g., if one word has a larger font size, more emphasis can begiven when associating the profile with keywords), or a hyperlinkstructure within the profile. The web crawler 202 can store thestatistical associations in a repository 212.

The advertisement server 108 receives the advertisements 214 via aninterface 216 and stores the advertisements 214 in the advertisementsinventory 114. The advertisements 214 include sub-components, includinga uniform resource identifier (URI), an image, a video, text, and/orkeywords. The image, video, and text can form the information displayedon the profile of a member 104. In addition, the advertisement 214 mayinclude audio or other appropriate media.

In certain implementations, the URI is a uniform resource link (URL)that permits a member 104 viewing the advertisement 214 to navigate fromthe profile of the member 104 to a web page of the advertiser. In otherimplementations, the URI can include contact information for theadvertiser (e.g., telephone number, mailing address, email address,etc.).

The community identifier 112 identifies the communities 106 of members104 based on the indexed content 208 and the member links 210. Theadvertisement to profile matcher 218 matches advertisements in theadvertisement inventory 114 to profiles of members 104 identified by thecommunity identifier 112 based on the indexed content 208, the memberlinks 210, statistical associations between keywords 212 and additionalkey words 220.

FIG. 3A depicts a schematic diagram of an example of a cluster of usersconnected by internal and external friendships. FIG. 3A shows a clusterof four users, A, B, C, and D. The solid lines represent declaredfriendships within the cluster and the dashed lines are friendshipsoutside of the cluster. The “weight” of the cluster is defined as thenumber of internal friendships divided by the total number offriendships. For the cluster in FIG. 1 the weight is 4/6 (or ⅔).

FIG. 3B depicts a schematic diagram of an example of a cluster of usersconnected by internal and external friendships. The influence score is ameasure that captures the general “influence” of a particular userrelative to the others members of a cluster. Higher scores mean moreinfluence. The influence score of a particular user can be calculated bymeasuring how the weight of the cluster changes by removing that user.FIG. 3B shows the cluster from FIG. 3A with user A removed. The weightof original cluster is ⅔, and the cluster in FIG. 3B has weight 2/6 (or⅓), thus the influence score of user A is ⅔−⅓=⅓.

FIG. 3C depicts a schematic diagram of an example of a cluster of usersconnected by internal and external friendships. FIG. 3C shows thecluster from FIG. 3A with user C removed. In this case, the cluster inFIG. 3C has a weight of ⅙. This makes the influence score of user Cequal to, 4/6−⅙= 3/6 (or ½). Intuitively, the influence score of aparticular user is related to how important they are in connecting theentire cluster. In FIG. 3A, it is clear that user C (influence score ½)plays a larger role in linking the four users than user A (influencescore ⅓).

Subsequent to identifying the communities 106, the advertisement server108 ranks the profile of each member 104 in the community 106. In oneimplementation, the web pages of each member 104 are ranked using themethod described in the patent titled “Method for node ranking in alinked database,” (U.S. Pat. No. 6,285,999 B1; inventor: Lawrence Page;date of patent: Sep. 4, 2001), the contents of which are incorporated byreference here.

Subsequent to ranking the profiles of the members 104 in each community106, the advertisement server retrieves advertisements 214 to display onthe profiles of one or all members 104 of the community 106. In oneimplementation, the advertisement server 108 identifies the one or morecommon interests of a community 106. In one aspect, the one or moreinterests may be identified based on the aggregate signals in thecommunities between members. In another aspect, the interest of a membermay be determined based on the content of the member's profile, such as,web page. In another aspect, the interest of the community may bedetermined from the context or textual content of the profiles of allthe members of the community.

The advertisement server 108 retrieves advertisements 214 relevant tothe one or more common interests of the community 106. The advertisementserver 108 transmits the advertisements 214 to the profiles of one ormore members 104 of the community 106 for display. In oneimplementation, the advertisements 214 are transmitted using the methoddescribed in United States patent application publication titled,“Methods and apparatus for serving relevant advertisements,”(publication no.: US 2004/0059708 A1, inventors: Jeffrey A. Dean,Georges R. Harik, Paul Bucheit, publication date: Mar. 25, 2004).

In one aspect, one or more advertisements are displayed on the profileof all members of a community. In another aspect, the number ofadvertisements displayed on a profile depends on the rank of the memberwithin the community. One or more members in a community may also bemembers of other communities. In another aspect, the advertisementsdisplayed on a member's profile may be blended to reflect the commoninterest of the one or more communities to which the member belongs.

FIG. 4 depicts a flow chart of an example of a method for advertising tocommunities in an online social network. The profiles of members of theonline social network are received by the advertisement server at 405.In one implementation, a server hosts the online social network 102 andthe profiles of the members. The profile for each member 104 comprisesone or more web pages. The server hosting the network also stores thelinks between the members 104 of the network 102. The content of theprofiles of the members and the links between the members 104 areincluded in the information transmitted by the server hosting the onlinesocial network and received by the advertisement server 108. Since thenumber of members in the network as well as the number and types oflinks between members is dynamic, the content of profiles and the linksbetween members is regularly monitored and updated. In one aspect, theserver hosting the network may monitor and update this information andtransmit the same to the advertisement server. In another aspect, theadvertisement server may monitor and update this information.

The interactions between members 104 are analyzed using graph theory andnetwork signals at 410. A member 104 is linked to one or more members104 through links of different types. The links from one member 104 haveassociated weights based on the number and the type of the links Oneexample of a link is a friendship link where members designates othermembers as friends. Another example of a link is fan link where membersdesignates themselves as fans of other members. A link may beestablished when a member frequently visits another member's profile. Inone aspect, the weights associated with links are a function of thenumber and the type of the links. In another aspect, weight isassociated based only on the number of links from a member. In anotheraspect, links are designated equal weight regardless of the type of thelink.

The members 104 of a community 106 establish links with one another dueto shared common interests. Using graph theory and the links betweenmembers 104, communities 106 of members 104 are identified at 415 suchthat each member 104 belongs to at least one community 106. In addition,a member may belong to more than one community. Subsequent toidentifying communities, the profiles of the members of each communityare examined to determine the common interest of the community. In oneaspect, the common interest is determined by examining the content ofthe profiles of each member of the community. In another aspect, thecontent of the profiles as well the aggregate signals within thecommunity are used to determine the common interest of the community.

The members 104 of a community 106 are ranked to identify one or moreinfluencers of the community at 420. Ranking assigns importance toprofiles of members in a linked database. In one implementation, therank assigned to a first member is calculated from the ranks of memberslinking to the first member. In one aspect, the member with the highestrank is designated as the influencer of the community. In anotheraspect, more than one member may be designated as influencers based ontheir rank.

Upon identifying communities 106 and designating influencers, the one ormore common interests of members 104 are determined and advertisementsrelevant to the interest of the community are displayed on the profilesof the members at 425. In one aspect, the advertisements relevant to theinterest of the community are displayed on the profiles of all themembers of the community. In another aspect, the advertisements relevantto the community may be displayed on the profiles of the one or moreinfluencers.

FIG. 5 depicts an example of profiles of members grouped into acommunity based on a common interest in action movies. In thisimplementation, the profiles of all members contain content related tomovies. In addition, the members establish links to one another byvisiting the profiles of other members, designating friendships, andcommenting on profiles. Based on the content of each profile and theaggregate signals between members, a community of movie fans isidentified. Advertisements displayed on the profiles of one or allmembers of this community may be related to action movies.

FIG. 6 depicts an example of blending advertisements for display on theprofile of a member belonging to more than one community. In thisimplementation, based on content of profiles and member interactions,member 1 belongs to community 1 and member 2 belongs to community 2.Member 3 belongs to both communities 1 and 2 due to one or moreoverlapping interests between communities. The advertisements displayedon the profile of a member on community 1 and 2 are relevant to theinterests of community 1 and 2, respectively. The blended advertisementsdisplayed on the profile of member 3 are relevant to the interests ofboth communities.

FIG. 7 depicts an example of designating one member as a networkinfluencer of a community. Member 1 is linked to all members of thecommunity 106 by the same or different types of links Due to the numberand the types of links, member 1 ranks highest among all other membersin the community. Therefore, member 1 is designated as the influencer ofthe community 106. Advertisers may target the entire community bydisplaying advertisements on the profile of member 1 alone.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications are possible. For example, theadvertisement server 108 can include a micropayment system. In oneimplementation, the micropayment system can track a number of times amember selects a URL in an advertisement on a member's profile. Insteadof charging the advertiser each time the URL is selected, themicropayment system can charge the advertiser after a charges associatedwith the clicks have crossed a predefined threshold, such as tendollars. Alternatively, the micropayment system can accept a paymentfrom an advertiser and create an account that the fees are debitedagainst. Accordingly, other implementations are within the scope of thefollowing claims.

In another implementation, a profile of a member may include web pagesnot associated with the online social network. These web pages may notinclude a standard structure of categories that describe a member. Amember may design a web page that includes a variety of contentincluding information about the member. For example, geocities.com hostsa variety of web sites that describe personal aspects of members of theweb hosting service. In one aspect, membership to a community may beindependent of web pages not associated with the online social network.In another aspect, the contents of the external web page may be used todetermine interest of the member. In another aspect, links by memberswithin the network to the external web pages may be ignored duringidentification of communities.

In another implementation, the contact information for a member may bedetermined from the member's profile. Subsequently, upon approval by themember, an advertiser may send advertisements to the member's contactinformation in lieu of or in addition to displaying advertisements on aweb page that the member frequently visits.

In another implementation, an advertiser may be a member of the onlinesocial network. An advertiser may display advertisements on theirprofiles. Members interested in the products and services beingadvertised may establish links with the advertiser's profile. In thismanner, an advertiser may attract consumers while also seeking outconsumers.

In addition, the logic flows depicted in the figures do not require theparticular order shown, or sequential order, to achieve desirableresults. In addition, other steps may be provided, or steps may beeliminated, from the described flows, and other components may be addedto, or removed from, the described systems. Accordingly, otherimplementations are within the scope of the following claims.

What is claimed is:
 1. A method comprising: identifying a communitywithin a network, wherein the community comprises a plurality of membersof the network; determining, by one or more computers, a weight of thecommunity based, at least in part, on a quantity of direct links amongthe members of the community and a quantity of indirect links among themembers of the community; for each member of the community, determining,by the one or more computers, a respective score of influence, at leastin part by determining a change between the weight of the community whenthe member is included in the community and a weight of the communitywhen the member is excluded from the community; identifying a pluralityof influencers in the community based, at least in part, on thedetermined scores of influence, wherein the plurality of influencers aremembers of the community with highest scores of influence; andassociating, by the one or more computers, advertisements with profilesof at least some the plurality of influencers.
 2. The method of claim 1,wherein at least two members of the plurality of members are linked toeach other directly and wherein at least two members of the plurality ofmembers are linked to each other indirectly, wherein the at least twoindirectly linked members are each linked to a common member of theplurality of members.
 3. The method of claim 2, wherein at least onemember of the at least two indirectly linked members is directly linkedto another member of the plurality of members.
 4. The method of claim 2,wherein at least one member of the at least two directly linked membersis indirectly linked to another member of the plurality of members. 5.The method of claim 2, wherein the common member is directly linked toanother member of the plurality of members.
 6. The method of claim 1,further comprising ranking each of the plurality of members included inthe community, wherein a rank of a member of the plurality of members isbased, at least in part, on the member's score of influence.
 7. Themethod of claim 6, wherein the identified plurality of influencerscorresponds to members of the community with highest ranks.
 8. Themethod of claim 1, wherein a quantity of advertisements associated withan influencer's profile depends upon the influencer's score ofinfluence.
 9. The method of claim 1, further comprising receivinginformation pertaining to a plurality of members belonging to thenetwork, wherein the information comprises content of profiles of theplurality of members and information describing one or more members withwhom each of the plurality of members is linked.
 10. The method of claim9, wherein identifying the community comprises grouping members who aredirectly and indirectly linked to each other.
 11. The method of claim10, wherein two members are connected to each other through a link thathas a weight, the method further comprising associating one or moreadvertisements with a profile of each of the two members, wherein aquantity of advertisements associated with the profile is based on theweight of the link.
 12. The method of claim 9, further comprisingidentifying one or more common interests of the plurality of members ofthe community based on the content of the profiles of the plurality ofmembers.
 13. The method of claim 9, wherein the network comprises anonline social network.
 14. The method of claim 13, wherein the profilecomprises one or more web pages stored on a server hosting the onlinesocial network.
 15. The method of claim 1, wherein a member of thenetwork belongs to more than one community.
 16. The method of claim 1,wherein associating advertisements with the profiles of the at leastsome of the plurality of influencers comprises placing theadvertisements on the profiles of the at least some of the plurality ofinfluencers.
 17. The method of claim 1, further comprising: determiningthat an influencer of the community is an influencer of anothercommunity within the network; and associating advertisements relevant tothe another community together with the advertisements with the profileof the influencer of the community and of the another community.
 18. Themethod of claim 1, further comprising iteratively optimizing thecommunity to account for addition and removal of members from theplurality of members.
 19. The method of claim 1, wherein a member'sscore of influence is further based on a quantity of members with whomthe member is directly linked and a quantity of members with whom themember is indirectly linked.
 20. A method comprising: identifying acommunity within a network, wherein the community comprises a pluralityof members of the network; determining, by one or more computers, aweight of the community by: identifying a first quantity of linksbetween the plurality of members in the community; identifying a secondquantity of links between members outside the community; and determiningthe weight of the community as a ratio of the first quantity to thesecond quantity; for each member of the community, determining, by theone or more computers, a respective score of influence, at least in partby determining a change between the weight of the community when themember is included in the community and a weight of the community whenthe member is excluded from the community; identifying a plurality ofinfluencers in the community based, at least in part, on the determinedscores of influence, wherein the plurality of influencers are members ofthe community with highest scores of influence; and associating, by theone or more computers, advertisements with profiles of at least some ofthe plurality of influencers.
 21. A non-transitory computer-readablemedium storing instructions executable by one or more processing devicesto perform operations for displaying advertisements to members of anetwork, the operations comprising: determining a weight of thecommunity based, at least in part, on a quantity of direct links amongthe members of the community and a quantity of indirect links among themembers of the community; for each member of the community, determininga respective score of influence, at least in part by determining achange between the weight of the community when the member is includedin the community and a weight of the community when the member isexcluded from the community; identifying a plurality of influencers inthe community based, at least in part, on the determined scores ofinfluence, wherein the plurality of influencers are members of thecommunity with highest scores of influence; and associatingadvertisements with profiles of at least some the plurality ofinfluencers.
 22. The medium of claim 21, wherein at least two members ofthe plurality of members are linked to each other directly and whereinat least two members of the plurality of members are linked to eachother indirectly, wherein the at least two indirectly linked members areeach linked to a common member of the plurality of members.
 23. Themedium of claim 21, the operations further comprising ranking each ofthe plurality of members included in the community, wherein a rank of amember of the plurality of members is based, at least in part, on themember's score of influence.
 24. The medium of claim 23, wherein theidentified plurality of influencers corresponds to members of thecommunity with highest ranks.
 25. The medium of claim 21, wherein aquantity of advertisements associated with an influencer's profiledepends upon the influencer's score of influence.
 26. The medium ofclaim 21, the operations further comprising receiving informationpertaining to a plurality of members belonging to the network, whereinthe information comprises content of profiles of the plurality ofmembers and information describing one or more members with whom each ofthe plurality of members is linked.
 27. The medium of claim 26, whereinidentifying the community comprises grouping members who are directlyand indirectly linked to each other.
 28. The medium of claim 27, whereintwo members are connected to each other through a link that has aweight, the method further comprising associating one or moreadvertisements with a profile of each of the two members, wherein aquantity of advertisements associated with the profile is based on theweight of the link.
 29. The medium of claim 26, the operations furthercomprising identifying one or more common interests of the plurality ofmembers of the community based on the content of the profiles of theplurality of members.
 30. The medium of claim 21, wherein associatingadvertisements with the profiles of the at least some of the pluralityof influencers comprises placing the advertisements on the profiles ofthe at least some of the plurality of influencers.
 31. The medium ofclaim 21, the operations further comprising: determining that aninfluencer of the community is an influencer of another community withinthe network; and associating advertisements relevant to the anothercommunity together with the advertisements with the profile of theinfluencer of the community and of the another community.
 32. The mediumof claim 21, the operations further comprising iteratively optimizingthe community to account for addition and removal of members from theplurality of members.
 33. The medium of claim 21, wherein a member'sscore of influence is further based on a quantity of members with whomthe member is directly linked and a quantity of members with whom themember is indirectly linked.